Triple
T1228590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal C (Luis Muñoz Marín International Airport) |
E26383
|
entity |
| Predicate | terminalNumber |
P24737
|
FINISHED |
| Object | C |
—
|
LITERAL 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: C | Statement: [Terminal C (Luis Muñoz Marín International Airport), terminalNumber, C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: terminalNumber Context triple: [Terminal C (Luis Muñoz Marín International Airport), terminalNumber, C]
-
A.
serviceNumber
Indicates a unique identifying number assigned to a service, used to reference, track, or distinguish that service from others.
-
B.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
C.
officeNumber
Indicates the specific room or suite number assigned to an office within a building or complex.
-
D.
portNumber
Indicates the specific communication port assigned to a network connection, service, or endpoint.
-
E.
terminusType
Indicates the specific kind or role of an endpoint or terminal within a route, network, or process.
- F. None of above. chosen
Provenance (4 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_69a49484688c8190a1bf285eb396a8b6 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be3c5d4c819087f9e9e37204c3be |
completed | March 1, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69a4bb65d61c8190bf0424ea0019a98b |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bbf83584819088c69366f58586cc |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:47 p.m.