Triple
T37643559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ortigas Center |
E936674
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object |
EDSA–Ortigas corridor
The EDSA–Ortigas corridor is a major urban and commercial stretch in Metro Manila where the busy EDSA thoroughfare intersects the Ortigas business district, forming one of the city’s key transport and economic hubs.
|
E2261124
|
NE FINISHED |
How this triple was built (2 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: EDSA–Ortigas corridor | Statement: [Ortigas Center, partOf, EDSA–Ortigas corridor]
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: EDSA–Ortigas corridor Triple: [Ortigas Center, partOf, EDSA–Ortigas corridor]
Generated description
The EDSA–Ortigas corridor is a major urban and commercial stretch in Metro Manila where the busy EDSA thoroughfare intersects the Ortigas business district, forming one of the city’s key transport and economic hubs.
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_69f76ed31d8881908405da6c6d2f0463 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba9843ae88190ad013b031b72241a |
completed | May 6, 2026, 8:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41852701608190b52cab3f46b40600 |
completed | June 28, 2026, 8:33 p.m. |
| NEDg | Description generation | batch_6a41868afbd48190abf6e272d867ecc6 |
completed | June 28, 2026, 8:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4186d235348190a89738f739b88fdb |
completed | June 28, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:18 p.m.