Triple
T18946800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurel, Batangas |
E463534
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Miguel Laurel
Miguel Laurel was a notable Filipino figure after whom the municipality of Laurel in Batangas, Philippines, was named.
|
E1432663
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Miguel Laurel | Statement: [Laurel, Batangas, namedAfter, Miguel Laurel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miguel Laurel Context triple: [Laurel, Batangas, namedAfter, Miguel Laurel]
-
A.
Miguel Leon
Miguel Leon is the central protagonist of the film "The Last Face," a humanitarian doctor navigating love and moral conflict amid the chaos of war-torn Africa.
-
B.
Miguel García
Miguel García is a common Spanish personal name shared by numerous individuals across fields such as sports, arts, and public life.
-
C.
Miguel Barragán
Miguel Barragán was a 19th-century Mexican politician and military leader who briefly served as president of Mexico during a period of intense political instability.
-
D.
Miguel Muñoz
Miguel Muñoz was a legendary Spanish footballer and manager best known for his long association with Real Madrid, both as a player and as one of the club’s most successful coaches.
-
E.
Miguel Sanchez
Miguel Sanchez is a musical track featured on the release "More Fish."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Miguel Laurel Triple: [Laurel, Batangas, namedAfter, Miguel Laurel]
Generated description
Miguel Laurel was a notable Filipino figure after whom the municipality of Laurel in Batangas, Philippines, was named.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Miguel Laurel Target entity description: Miguel Laurel was a notable Filipino figure after whom the municipality of Laurel in Batangas, Philippines, was named.
-
A.
Miguel Leon
Miguel Leon is the central protagonist of the film "The Last Face," a humanitarian doctor navigating love and moral conflict amid the chaos of war-torn Africa.
-
B.
Miguel García
Miguel García is a common Spanish personal name shared by numerous individuals across fields such as sports, arts, and public life.
-
C.
Miguel Barragán
Miguel Barragán was a 19th-century Mexican politician and military leader who briefly served as president of Mexico during a period of intense political instability.
-
D.
Miguel Muñoz
Miguel Muñoz was a legendary Spanish footballer and manager best known for his long association with Real Madrid, both as a player and as one of the club’s most successful coaches.
-
E.
Miguel Sanchez
Miguel Sanchez is a musical track featured on the release "More Fish."
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5402ad881908add559249278895 |
completed | April 20, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a088af613dc81908d346f4df93f05fa |
completed | May 16, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_6a088c0f55348190b33ab62ed033ed39 |
completed | May 16, 2026, 3:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088d00188c8190bb8da973f31b1e47 |
completed | May 16, 2026, 3:28 p.m. |
Created at: April 10, 2026, 11:59 a.m.