Triple
T1249970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas School Book Depository |
E26851
|
entity |
| Predicate | floorCountRelevantToEvent |
P1728
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Texas School Book Depository, floorCountRelevantToEvent, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: floorCountRelevantToEvent Context triple: [Texas School Book Depository, floorCountRelevantToEvent, 6]
-
A.
floorCount
Indicates the number of floors or levels that a building or structure has.
-
B.
numberOfFloors
chosen
Indicates the total count of distinct floor levels that a building or structure has.
-
C.
numberOfFloorsServed
Indicates the total count of distinct floors that are served or accessed by a given entity (such as an elevator or service system).
-
D.
hasFloor
Indicates that one entity possesses, includes, or is associated with a particular floor or level within a structure.
-
E.
numberOfStairs
Indicates the quantity of stairs associated with or present in a given context or structure.
- F. None of above.
Provenance (3 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_69a49487a9c48190ba9b05348fd1b53f |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf84c73c8190bbb14265cd7ab6ae |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6b075881908e867c25b5080e25 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.