Triple
T9123412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LIP |
E218913
|
entity |
| Predicate | usedInTown |
P87209
|
FINISHED |
| Object |
Schlangen
Schlangen is a small municipality in the Lippe district of North Rhine-Westphalia, Germany, known for its rural character and proximity to the Teutoburg Forest.
|
E779635
|
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: Schlangen | Statement: [LIP, usedInTown, Schlangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schlangen Context triple: [LIP, usedInTown, Schlangen]
-
A.
The Snake
The Snake is a crime novel featuring hard-boiled private detective Mike Hammer, created by American author Mickey Spillane.
-
B.
The Snakes
The Snakes is a subgroup affiliated with the Brooklyn hip-hop collective Junior M.A.F.I.A., associated with the mid-1990s East Coast rap scene.
-
C.
La Vibora
La Vibora is a bobsled-style steel roller coaster at Six Flags Over Texas known for its winding, trackless chute that simulates a high-speed toboggan run.
-
D.
Lagarto
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
-
E.
Slither
Slither is a 2006 horror-comedy film that blends grotesque alien invasion elements with dark humor and cult-movie sensibilities.
- 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: Schlangen Triple: [LIP, usedInTown, Schlangen]
Generated description
Schlangen is a small municipality in the Lippe district of North Rhine-Westphalia, Germany, known for its rural character and proximity to the Teutoburg Forest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schlangen Target entity description: Schlangen is a small municipality in the Lippe district of North Rhine-Westphalia, Germany, known for its rural character and proximity to the Teutoburg Forest.
-
A.
The Snake
The Snake is a crime novel featuring hard-boiled private detective Mike Hammer, created by American author Mickey Spillane.
-
B.
The Snakes
The Snakes is a subgroup affiliated with the Brooklyn hip-hop collective Junior M.A.F.I.A., associated with the mid-1990s East Coast rap scene.
-
C.
La Vibora
La Vibora is a bobsled-style steel roller coaster at Six Flags Over Texas known for its winding, trackless chute that simulates a high-speed toboggan run.
-
D.
Lagarto
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
-
E.
Slither
Slither is a 2006 horror-comedy film that blends grotesque alien invasion elements with dark humor and cult-movie sensibilities.
- 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_69ca83dddd548190983b96c664f7f367 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8b5fa188190be6465e74cf26915 |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0308ff628819083f02bf71eb40c5b |
completed | April 3, 2026, 9:26 p.m. |
| NEDg | Description generation | batch_69d0318ef52c8190bfa0bef6a8d41daa |
completed | April 3, 2026, 9:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d03571c4648190bd546152c61c55a5 |
completed | April 3, 2026, 9:47 p.m. |
Created at: March 30, 2026, 7:17 p.m.